Test of random effects with incomplete panel data

This paper examines properties of test statistics for random effects with incomplete panel data. We can divide incomplete panel data into two groups. One group arises from randomly missing or unbalanced data and the other arises from systematically missing data. We focus on the former case. Some statistical properties when there are missing independent variables in regression analysis are well known. A simple approach to treat missing observations is to just discard the missing cases, but such approach may be highly inefficient. In this paper, instead of discarding the missing cases, we consider the missing data to be the outcome of a random variable. The test statistic for random effects with randomly missing panel data is derived. We examine the statistical properties of the derived test statistic and compare it with test statistic derived without randomness. We find that our test statistic is conservative in comparison with the test statistic derived without randomness. (C) 2003 IMACS. Published by Elsevier B.V. All rights reserved.